Qisong Wu

Southeast University

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Moeness G. Amin

Villanova University

Yimin D. Zhang

Villanova University

Zheng Bao

Anhui University of Technology

Braham Himed

United States Air Force Research Laboratory

Mengdao Xing

Xidian University

Shuai Yao

Southeast University

Fauzia Ahmad

Temple University

Liang An

Southeast University

Zhichao Lai

National Taipei University of Technology

Dixian Zhao

Southeast University

Cooperative Institutions

Southeast University

Chinese Academy of Sciences

Xidian University

Chinese Academy of Medical Sciences & Peking Union Medical College

Zhejiang University

Signal Processing (United States)

Sun Yat-sen University

Shanghai University

Central South University

University of Chinese Academy of Sciences

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Noise Psd Estimation Based on Time-Frequency Signal Presence Probability Sensing For Noisy Multi-Component Underwater Acoustic Pulse Signal

Shuai Yao Qingyun Kuang Qiwei Liu Chenxin Xiao Qisong Wu

Accurately estimating noise power spectral density (PSD) is crucial for multi-component underwater acoustic pulse (MC-UAP) signal enhancement. This paper introduces an innovative noise PSD estimation technique based on time-frequency signal presence probability (SPP) sensing. This method seamlessly integrates with any non-cooperative MC-UAP signal enhancement algorithm requiring a noise PSD estimate. Unlike conventional methods, where estimating noise spectrum involves a weighted average of past noise estimates and the current observed signal spectrum, the proposed approach doesn't rely on a time and single-frequency-dependent weight factor. Instead, it employs a unique time and multi-frequency-dependent weight factor, akin to a time-frequency SPP. To sense the SPPs of non-cooperative MC-UAP signals, we define and extract two key time-frequency characteristics: the time-dependent energy fluctuation characteristic (TDEFC) and the frequency-dependent short-time narrowband characteristic (FDSTNBC). These characteristics enable us to derive time-frequency-dependent SPPs. Our proposed method's superior performance over existing state-of-the-art algorithms is verified through simulations and experiments with sea-truth data.

SIGNAL (programming language)

Component (thermodynamics)

Underwater Acoustics

10.2139/ssrn.4650217

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Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning & Mixture Modeling

arXiv (Cornell University) (2013)

David Carlson Joshua T Vogelstein Qisong Wu Wenzhao Lian Mingyuan Zhou

We propose a construction for joint feature learning and clustering of multichannel extracellular electrophysiological data across multiple recording periods for action potential detection and discrimination ("spike sorting"). Our construction improves over the previous state-of-the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) elegantly deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage ("frequentist") learning process. Fourth, by directly modeling spike rate, we improve detection of sparsely spiking neurons. Moreover, our Bayesian construction seamlessly handles missing data. We present state-of-the-art performance without requiring manually tuning of many hyper-parameters on both a public dataset with partial ground truth and a new experimental dataset.

Spike sorting

Feature (linguistics)

10.48550/arxiv.1304.0542

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Asprosin is a centrally acting orexigenic hormone

Yearbook of pediatric endocrinology (2018)

Clemens Duerrschmid Yan He C Wang Li C Bournat JC

This study highlights the importance of asprosin in the regulation of appetite. This peptide hormone was first described by Romere et al. in 2016 (1), who reported 2 patients with neonatal progeroid syndrome (NPS) due to truncating heterozygous mutations in the fibrillin-gene (FBN1). The FBN1 gene encodes profibrillin which is cleaved by the protease furin, yielding mature fibrillin and a C-terminal cleavage product, asprosin. The mutation led to a 50% reduced asprosin concentration in serum, associated with extreme leanness. Usually, partial or general lipodystrophy leads to insulin resistance, which is not the case in these patients (1, 2). Asprosin is highly expressed in adipose tissue and its secretion stimulates hepatic glucose release and acts as an orexigenic agent in the hypothalamus (1). Like leptin, asprosin concentration rises with fasting and decreases with refeeding.

Orexigenic

Furin

10.1530/ey.15.11.12

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Citations (27)

Multi-Task Bayesian compressive sensing exploiting signal structures

Signal Processing (2020)

Jiahao Liu Qisong Wu Moeness G. Amin

SIGNAL (programming language)

10.1016/j.sigpro.2020.107804

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Citations (13)

Large-scale sparse reconstruction through partitioned compressive sensing

Si Qin Yimin D. Zhang Qisong Wu Moeness G. Amin

Compressive sensing (CS) finds broad applications in various sparse reconstruction problems. It has been clearly established that CS techniques achieve improved quality and resolution for many radar imaging problems where the scene is sparse or can be sparsely represented. One of the major issues that limits the applicability of CS techniques in radar systems is the prohibitive complexity in large-scale imaging problems encountered in, for example, synthetic aperture radar. However, as the actual scene and the back-projection images are associated with the point spreading function which has a finite support, it becomes possible to reconstruct the sparse scene based only on local observations. In this paper, we develop a novel segmented CS technique that achieves nearly optimal sparse reconstruction performance with significant reduction of computation complexity and memory requirements. The effect of interference from neighboring segments is examined, and the conditions of interference-free reconstruction of segmented compressive sensing are devised. The effectiveness of the proposed technique is verified by simulation results.

10.1109/icdsp.2014.6900784

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Citations (6)

Structured Bayesian compressive sensing with spatial location dependence via variational Bayesian inference

Digital Signal Processing (2017)

Qisong Wu Shiliang Fang

Snapshot (computer storage)

10.1016/j.dsp.2017.08.007

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Citations (9)

Autofocus Bayesian compressive sensing for multipath exploitation in urban sensing

Qisong Wu Yimin D. Zhang Moeness G. Amin Fauzia Ahmad

Exploitation of group sparsity under multipath propagation enables high-resolution ghost-free imaging in urban sensing and through-the-wall radar imaging applications. Multipath exploitation schemes typically require exact prior information of the indoor scene layout and transceiver locations to eliminate ghosts targets. Imperfections in the prior knowledge lead to performance degradation of such schemes. In this paper, a novel autofocus Bayesian compressive sensing approach is proposed for joint scene reconstruction and correction of phase errors resulted from transceiver position uncertainties. Supporting simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

Autofocus

Transceiver

10.1109/icdsp.2015.7251834

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Citations (2)

スパースBayes学習を用いたマルチスタティック受動レーダにおける空間‐時間適応処理と動きパラメータ推定【Powered by NICT】

IEEE Transactions on Geoscience and Remote Sensing (2016)

Qisong Wu D Zhang Yimin G Amin Moeness Braham Himed

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Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar

Sensors (2020)

Qisong Wu Teng Gao Zhichao Lai Dianze Li

Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the F 1 score of 0.90 and area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.99 over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar.

Robustness

Feature (linguistics)

10.3390/s20123504

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Citations (26)

[Effect of ACTH on the expression of somatostatin and c-fos in the spinal cord and formalin evoked pain response].

PubMed (1999)

Qisong Wu XC Li Ruan Hz Li Hd

In the present study, the effect of ACTH on the expression of somatostatin and c-fos in the spinal cord and formalin induced pain responses in rats were studied using immunohistochemistry and in situ hybridization. The results showed that subcutaneous injection of formalin in the right hindpaw increased c-fos-like immunoreactivity (FLI), somatostatin-like immunoreactivity (Som-LI), Som-LI/FLI and perprosomatostatin mRNA (PPS-mRNA) in neurones of right spinal dorsal horn and significantly enhanced pain intensity rating. ACTH decreased the FLI,Som-LI, Som-LI/FLI and PPS-mRNA levels of the spinal cord evoked by formalin. The decrease of c-fos or Som level due to intrathecal injection of ACTH in rats with chronic pain was prevented by injection of cyproheptadine, but not by bicuculline and naloxone. The results indicate that the serotonin receptor may be involved in ACTH induced analgesia.

c-Fos

Subcutaneous injection

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